By compensating transmission signals for distortion using digital signal processing, coherent optical communication realizes large-capacity transmission on the order of tens of Gbits/s or more. The digital signal processing involves chromatic dispersion compensation, polarization demultiplexing/polarization mode dispersion compensation, frequency and phase shift compensation, and other processes. For further increases in capacity, there is demand to further improve compensation accuracy of the above processes.
Adaptive equalization, which is the most important function of digital signal processing, compensates a time varying situation using mainly polarization demultiplexing/polarization mode dispersion compensation, residual chromatic dispersion compensation, timing jitter compensation, and the like.
An adaptive equalizer is generally made up of a digital filter, which allows transmission signals to be compensated when tap coefficients capable of canceling distortion of the transmission signals are set on the digital filter. Thus, compensation accuracy depends on appropriateness of the tap coefficients. Various algorithms have been proposed conventionally as methods for calculating the tap coefficients.
For example, an improved version of decision directed least mean square (DD-LMS) has been proposed as an adaptive equalization algorithm, (see, for example, PTL 1). However, because the proposed technique uses a numerically-controlled oscillator in a decision-feedback loop and cannot follow phase noise variation of laser, it is difficult to implement the algorithm in a circuit.
Also, an improved version of the constant modulus algorithm (CMA) has been proposed as an adaptive equalization algorithm (see, for example, PTL 2). This method updates the tap coefficients of the filter in the adaptive equalizer such that an amplitude modulation component of an output signal will be constant, but becomes difficult to use when a multi-value level of a modulation method increases. Also, although improvements are being made towards value multiplexing, further value multiplexing is difficult.
Also, an improved version of the least mean square (LMS) algorithm has been proposed as an adaptive equalization algorithm (see, for example, PTL 3). In this method, a fixed filter corresponding to band limiting is inserted into a feedback loop.